Abstract: A method of treating prostate cancer in a patient comprising administering to the patient a pharmaceutical composition including a first therapeutic including a TLK1B inhibitor, or a pharmaceutically acceptable salt, solvate, ester, amide, clathrate, stereoisomer, enantiomer, prodrug or analog thereof, and a second therapeutic including an antiandrogen or a pharmaceutically acceptable salt, solvate, ester, amide, clathrate, stereoisomer, enantiomer, prodrug or analog thereof.

Abstract: A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data.

Abstract: A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data.

Abstract: A method of treating prostate cancer in a patient comprising administering to the patient a pharmaceutical composition including a first therapeutic including a TLK1B inhibitor, or a pharmaceutically acceptable salt, solvate, ester, amide, clathrate, stereoisomer, enantiomer, prodrug or analog thereof, and a second therapeutic including an antiandrogen or a pharmaceutically acceptable salt, solvate, ester, amide, clathrate, stereoisomer, enantiomer, prodrug or analog thereof.

Abstract: A method of treating prostate cancer in a patient comprising administering to the patient a pharmaceutical composition including a first therapeutic including a TLK1B inhibitor, or a pharmaceutically acceptable salt, solvate, ester, amide, clathrate, stereoisomer, enantiomer, prodrug or analog thereof, and a second therapeutic including an antiandrogen or a pharmaceutically acceptable salt, solvate, ester, amide, clathrate, stereoisomer, enantiomer, prodrug or analog thereof.

Abstract: A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data.

Abstract: A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data.

Abstract: A spirit level comprising a generally central elongate bar (1), and at least one pair of arm members (6) pivotally coupled at a respective end of said bar for movement between a first, extended, generally H-shaped configuration in which said arm members (6) extend from said end of side bar at an angle thereto, and a second folded configuration, in which a side-wall of each of said arm members (6) is substantially parallel to and located against or adjacent to a side edge of said central bar, wherein at least one bulb (2) containing a bubble is provided in or on said central bar (1) and oriented substantially parallel to its longitudinal axis, and at least one bulb (2) containing a bubble is provided in or on an arm member (6) and oriented substantially parallel to its longitudinal axis.

Abstract: A method of treating prostate cancer in a patient comprising administering to the patient a pharmaceutical composition including a first therapeutic including a TLK1B inhibitor, or a pharmaceutically acceptable salt, solvate, ester, amide, clathrate, stereoisomer, enantiomer, prodrug or analog thereof, and a second therapeutic including an antiandrogen or a pharmaceutically acceptable salt, solvate, ester, amide, clathrate, stereoisomer, enantiomer, prodrug or analog thereof.

Abstract: A spirit level comprising a generally central elongate bar (1), and at least one pair of arm members (6) pivotally coupled at a respective end of said bar for movement between a first, extended, generally H-shaped configuration in which said arm members (6) extend from said end of side bar at an angle thereto, and a second folded configuration, in which a side-wall of each of said arm members (6) is substantially parallel to and located against or adjacent to a side edge of said central bar, wherein at least one bulb (2) containing a bubble is provided in or on said central bar (1) and oriented substantially parallel to its longitudinal axis, and at least one bulb (2) containing a bubble is provided in or on an arm member (6) and oriented substantially parallel to its longitudinal axis.

Abstract: The present invention provides for the treatment of multiple sclerosis by combination therapies of CD52 specific antibodies, such as CAMPATH-1H, with either a Type 1 interferon or coploymer-1.